Mobile Technology for Empowering Health Workers in Underserved Communities: New Approaches to Facilitate the Elimination of Neglected Tropical Diseases
Bibliographic record
Abstract
BACKGROUND: As global mobile phone penetration increases, direct health information communication from hard-to-reach communities is becoming commonplace. Mobile health (mHealth) tools that enable disease control programs to benefit from this information, while simultaneously empowering community members to take control of their own health, are vital to the goal of universal health care. OBJECTIVE: Our aim was to highlight the development of the Liverpool mHealth Suite (LMS), which has been designed to address this need and improve health services for neglected tropical diseases being targeted for global elimination, such as lymphatic filariasis. METHODS: The LMS has two main communication approaches-short message service and mobile phone apps-to facilitate real-time mass drug administration (MDA) coverage, reporting patient numbers, managing stock levels of treatment supplies, and exchanging health information to improve the quality of care of those affected. RESULTS: The LMS includes the MeasureSMS-MDA tool to improve drug supplies and MDA coverage rates in real-time (currently being trialed in urban Tanzania); the MeasureSMS-Morbidity tool to map morbidity, including lymphedema and hydrocele cases (initially piloted in rural Malawi and Ghana, then extended to Ethiopia, and scaled up to large urban areas in Bangladesh and Tanzania); the LyMSS-lymphedema management supply system app to improve distribution of treatments (trialed for 6 months in Malawi with positive impacts on health workers and patients); and the HealthFront app to improve education and training (in development with field trials planned). CONCLUSIONS: The current success and scale-up of the LMS by many community health workers in rural and urban settings across Africa and Asia highlights the value of this simple and practical suite of tools that empowers local health care workers to contribute to local, national, and global elimination of disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".